A health IT startup has developed a search tool that can speed up and customize searches across multiple electronic health records so providers can prioritize certain patient criteria to improve care and reduce medical errors.
Sam Rakowski died four days after being admitted to Bridgeport Hospital in the Yale New Haven Health System, an event CEO Matt Walton said could have been avoided had the hospital staff had access to data contained within the patient’s EMR. Since then, the company has worked with the provider to develop its SAMI system to reduce the risk of medical errors.
Walton makes the point that it’s not enough for a provider to have an EMR system if that data can’t be easily shared by the people responsible for managing a patient’s care.
It uses a faceted search system to make searching for data across multiple EMRs easier and faster. Currently, says Walton, hospitals tend to search for patient information one EMR at a time. He likens its faceted search tool to vacation booking website KAYAK — where information is grabbed from several websites at one time to compare prices for flights, hotels and car rentals. To address the issue of interoperability, Walton said the system can be used with the five legacy EMR platforms:SEM Allscripts, Meditech, GE Centricity, PICIS, Cerner and EPIC as well as inpatient and ambulatory systems.
Taking a page from iTunes, custom searches can be saved as a “clinical playlist,” using criteria the user deems important. It can also be shared with other users across multiple hospitals. The company is also in discussions about using the tool with heath information exchanges. Increased sharing of certain patient data between institutions is among the criteria providers are being asked to meet as part of the Affordable Care Act.
Walton said its SAMI system has applications for the clinical trial space and has implications for personalized medicine. “We’re engaged in active discussions to create a genetic care interchange involving institutions around the world.”
Walton explained that its search tool could be used to identify people with a particular type of cancer and could search for particular genetic profiles that could be matched with people who have responded well to certain cancer therapies.
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